The Electrical and Mechanical Services Department has successfully replaced the chiller plant at Tai Lung Veterinary Laboratory as part of its drive to achieve carbon neutrality. It was proposed that Artificial Intelligence (AI) models could be employed to optimise the control logic of the chiller plant to enhance its efficiency. Instead of relying on mathematical models based on the physical principles, the optimisation approach is constructed using data-driven models developed from previous operational data collected from the chiller plant. The performance of the optimisation algorithm was assessed through the utilisation of data from 2023. Statistical analysis was implemented to determine the extent to which the chiller plant improved, with respect to both energy consumption and the coefficient of performance. The chiller plant optimisation with partially observable reinforcement learning (RL) algorithm has been proposed. This technique involves the development of three machine learning models including forecast cooling demand, predict cooling load, and predict energy consumption. The resultant cooling load prediction model and energy consumption prediction model establish a simulation environment for RL agents to explore and it allows them to learn and adapt the dynamic control policies while fulfilling the cooling load requirements.
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